1 Objectives
2 General Comments on the Script
3 Chromatin States defined in the manuscript
4 Set the working environment and Folder to output the results
- 4.1 Set processors
5 Load motifs to make the name in the matrix
6 Identification of highly variable TFs
- 6.1 Plot from TOP N° of TFs per peaks
- 6.2 Plot from TOP TFs in the data
7 Perform normalization and clustering
8 Run the dimnsionality reduction by UMAP and T-SNE
- 8.1 Plot UMAP and TSNE by Clusters
- 8.2 Plot UMAP and TSNE by GROUP
9 Find top TFs for every Neighborhood ZONE
- 9.1 Barplot across the Neighborhood by GROUP
- 9.2 Heatmap of the top 10 markers per neighborhood after the Likelihood-ratio test
10 Handeling of the Seurat Object after Analysis
- 10.1 Extract analized matrix from Seurat objects
- 10.2 Selection of candidate genes from 04dpci marker list. This is performed after checking the output excel file
11 Code to perform Violin plots of selected TFs
12 Dimentional reduction plot for each selected TFs
- 12.1 UMAP plot
- 12.2 TSNE plot
13 SessionInfo

Mailto: julio.cordero@medma.uni-heidelberg.de

1 Objectives

To analyze the TFs-Motifs associated with the chromatin States areas as scRNA-seq
To find Transcriptional Factor in neighborhood Zone (TFinZONE)

2 General Comments on the Script

-Instead of an expression of genes, we propose an enrichment score. Which combines the motif score of the TFs and the log10-Pvalue of the motif in the chromatin state. Each Cluster identified represents a group of PEAKS which contain a specific signature of MOTIFS from TFs. - Allow the identification of TFs in the neighborhood Zone. To understand the mechanism of how TFs, cooperate among them. -This Script was used with the Motifs associated with Histone marks. However, it could also be modified to input Motifs associated with ATAC-seq or ChIP-seq. In the case of ATAC-seq, the motifs coming from footprinting analysis or Homer motifs search are highly recommended.

3 Chromatin States defined in the manuscript

4 Set the working environment and Folder to output the results

It is important to keep the output files well organized following the FAIR principles
Findable, Accessible, Interoperable, and Reusable (FAIR)

 workdir = "./"
setwd(workdir)
PTHA1="../03OUTPUT/"
dir.create(PTHA1)
PORT=PTHA1
TRY="_"
TRY1="02Ia_"
PROJECT="03b_TFinZONE_at_14dpci"
PVALUE=0.05
#### FOR VOLCANO ###
upcol<- "#B2182B" # magenta from PiyG
nc<- "#000000" # black
downcol<- "grey" # green from PiyG
CC= c(downcol, "#F7F7F7",upcol)
BASIC_COL="black"
CO3<- c("lightgrey","#B2182B")
CO_ALU3=c("#d7191c","#d8b365","#542788","grey", "#91bfdb", "grey","grey")

NAME33<- c("Mo_Name","Consensus","P-value","Log_P-value","q-value_Benja","Nor_of_Tar","Per_of_Tar","No_of_Tar_Backg","Per_of_Tar_Backg")
NAME34<- c("HOmer_NAME",NAME33)
mat_m<- data.frame("mask_500_FIND")
## Here you write the Path to the 
WORK1="../01DATA_ORI/08MO_onP/"

#WORK1="Y:/002ZF/000FIGURES_ANALY/00GITHUB_01HIS_Chroma_Factors/01HIS_Chrom_Factors_TEST/04MOtifs_Analy_as_SC_MOAC_Fig3_5/02Fig4_MO_MOAC_14dpci/14dATA_ORI/08MOTIFS_on_PEAKS/"
NAME_G2 <-data.frame(list.files(path=WORK1,pattern="*_FIND"))

colnames(NAME_G2)<- "ENH4"
#NAME_G2$GROUP <- gsub("CORR1_14d_", "", NAME_G2$ENH4)
NAME_G2$GROUP <- gsub("*_mask_500_FIND", "", NAME_G2$ENH4)
### this is important to check the SYMBOL
NAME_G22<- data.frame(c("00A_Ia","01Ia_A","02U_Ea","03U_Ia","04U_Pa"))


NAME_G2c<- as.character(t(NAME_G2[,2]))

#WORK11A11="../14dATA_ORI/07aMO_forMOAC/08TSS_Enh_w_14dpci_fHeatmap_ALL.xlsx"
WORK11A11="../01DATA_ORI/07aMO_forSeurat_ANALY/08TSS_Enh_w_14dpci_fHeatmap_ALL.xlsx"


MAT_1A= read_excel(WORK11A11)
#MAT_1A= read.delim(WORK11A11)

colnames(MAT_1A)[1]<- "SYMBOL"

dim(MAT_1A)

## [1] 329  11

##### Set resolution for CLustering
RES=0.5

4.1 Set processors

## sequential:
## - args: function (..., envir = parent.frame())
## - tweaked: FALSE
## - call: NULL

## sequential:
## - args: function (..., workers = 16, envir = parent.frame())
## - tweaked: TRUE
## - call: plan("sequential", workers = 16)

5 Load motifs to make the name in the matrix

5.1 Gather the matrix from all the TFs found in this stage

5.2 Merge TFs Motifs from the 5 GROUPs (00A_Ia, 01Ia_A, 02U_Ea, 03U_Ia, 04U_Pa)

Matrix of TFs by Peaks, could be used to merge with other time points

## 
## 00A_Ia_14dcpi 01Ia_A_14dcpi 02U_Ea_14dcpi 03U_Ia_14dcpi 04U_Pa_14dcpi 
##        419729          2370          6786         64905         13643

## [1] 3

5.3 TF Matrix as Single cell analysis matrix

5.4 Create a Seurat object

b7<- AC_RE_VOL_SC_META
rownames(b7)<- b7$PositionID2

mat_c1<- mat_cm_TMM_BOTH_SC[,rownames(b7)]
rownames(mat_c1)<- mat_cm_TMM_BOTH_SC$SYMBOL
dim(mat_c1)

## [1]  217 3746

dim(b7)

## [1] 3746    5

smartseq2 <- CreateSeuratObject(mat_c1,project = "14d", meta.data = b7)

6 Identification of highly variable TFs

## png 
##   2

6.1 Plot from TOP N° of TFs per peaks

arrangeQC_TF <- ggarrange(EC00,EC001, ncol = 2,nrow =1,common.legend = T, align = c("hv"),legend="none")

print(arrangeQC_TF)

6.2 Plot from TOP TFs in the data

grid.arrange(  plot1,plot2, ncol = 1,nrow = 2)

7 Perform normalization and clustering

## Centering and scaling data matrix

7.1 Run the Linear dimensional reduction

## PC_ 1 
## Positive:  Nanog, Hoxa9, Smad3, Tbx5, Hoxa11, Hoxd12, Tgif2, Lhx3, TRPS1, Twist2 
##     PR, Pitx1, GATA3, NeuroG2, DLX2, TCF4, Sox21, Hoxd11, Olig2, Sox3 
##     BHLHA15, BMAL1, LHX9, NPAS, HOXB13, Sox6, Smad4, Smad2, Sox10, Hoxa13 
## Negative:  En1, Hoxd10, Hoxa10, CUX1, LEF1, Ap4, THRb, HNF6, Elk4, Foxh1 
##     Myf5, PRDM10, EBF1, IRF4, ELF1, ZNF467, Unknown, ETS, TCFL2, HOXA2 
##     E2F6, IRF3, E2F3, Gfi1b, IRF2, YY1, Npas4, VDR, RAR, Six4 
## PC_ 2 
## Positive:  GABPA, ERG, Etv2, ETV1, Fli1, ETV4, ETS1, Elf4, EWS, EHF 
##     ELF3, ELF5, Elk1, SPDEF, ELF1, ETS, Elk4, Bcl6, IRF8, Stat3 
##     STAT4, Ap4, RORa, RARa, Hoxa10, STAT1, PPARE, Tbx21, EBF1, En1 
## Negative:  Foxf1, FoxL2, Foxa3, FOXK1, Foxo3, FOXP1, FOXA1, Foxa2, FOXK2, Fox 
##     FOXM1, CDX4, BMAL1, NPAS2, FoxD3, MNT, NPAS, Usf2, Hoxa11, DLX2 
##     Foxo1, Hoxd13, LHX9, CLOCK, Hoxa13, USF1, HOXB13, NFIL3, Hoxd11, MITF 
## PC_ 3 
## Positive:  RARa, Erra, Zac1, THRb, Sox21, Gata4, Sox3, Isl1, Sox2, Sox10 
##     Bapx1, Gata1, Esrrb, PPARa, BMAL1, EAR2, Sox6, MNT, Sox15, Mef2b 
##     TEAD3, Gata2, Gata6, ZNF416, NPAS, Sox9, GATA3, ERRg, Gfi1b, Nanog 
## Negative:  Foxo3, FOXK1, Foxf1, FOXP1, FoxL2, Foxo1, FOXA1, Foxa2, FOXK2, Elk4 
##     ELF1, Elk1, ETS, Foxa3, Npas4, FOXM1, CUX1, Ap4, HNF6, Myf5 
##     Fox, Foxh1, IRF4, FoxD3, Cdx2, Fli1, Hoxd13, IRF3, GABPA, Eomes 
## PC_ 4 
## Positive:  En1, Sox2, Sox15, Hoxd10, FOXM1, THRb, RARa, Sox6, Sox10, FOXA1 
##     Hoxa10, Fox, Sox3, Sox21, Sox17, Foxa2, STAT4, Sox4, FoxL2, Sox7 
##     FOXK2, Erra, Foxa3, Sox9, Isl1, Bcl6, FoxD3, PRDM10, EWS, EBF1 
## Negative:  Atf3, BATF, Fra1, Fos, JunB, Fra2, Atoh1, Twist2, TCF4, BHLHA15 
##     NeuroG2, Npas4, Elk4, Ap4, ELF1, Tcf21, ETS, Elk1, Myf5, CUX1 
##     HNF6, Hoxd13, Foxh1, Cdx2, IRF4, E2F4, IRF3, Olig2, LEF1, MNT 
## PC_ 5 
## Positive:  Fra1, Atf3, Fra2, JunB, Fos, BATF, RARa, En1, FOXM1, Foxa2 
##     FOXA1, Fox, Foxa3, Hoxd10, THRb, Sox2, Sox15, FoxL2, Hoxa10, Sox10 
##     Sox6, Erra, Sox9, Sox17, Sox3, FoxD3, FOXP1, Isl1, FOXK2, Sox7 
## Negative:  CLOCK, Max, bHLHE40, USF1, MNT, BMAL1, NPAS2, NPAS, bHLHE41, Usf2 
##     Elk4, Atoh1, ELF1, MITF, Ap4, Npas4, ETS, Elk1, CUX1, Myf5 
##     TCF4, HNF6, Twist2, NeuroG2, BHLHA15, RUNX1, Tcf21, RUNX, Foxh1, RUNX2

## Plot Pincipal component analysis (PCA)

grid.arrange(  P1, ncol = 1,nrow = 1)

7.2 Plot heatmap from 200 Peaks by Pincipal Component Analysis PCA

print(P2)

## png 
##   2

7.3 Perform Unsupervised Clusters analysis of TFs on peaks

set.seed(1)
smartseq2 <- FindNeighbors(smartseq2, dims = 1:10)
smartseq2 <- FindClusters(smartseq2, resolution = RES, random.seed= 1, algorithm=1)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 3746
## Number of edges: 115000
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8731
## Number of communities: 10
## Elapsed time: 0 seconds

8 Run the dimnsionality reduction by UMAP and T-SNE

CO<-brewer.pal(n = 12, name = "Paired")
COb<-brewer.pal(n = 8, name = "Dark2")
CO2b<-c("#E66100","#000000","#0affc2")
CO2<-c("#b2182b","#ef8a62","#67a9cf")
##EDIT Paired color
COe2<- c("#88CCEE", "#332288","#AA4499", "#44AA99", "#999933", "#882255", "#661100", "#6699CC","#888888","#000000","#77ddcc","#df65b0","#CC79A7","#332288", "#E66100", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#A6CEE3", "#FDBF6F", "#FF7F00","#CAB2D6","#F0E442","#B15928","#7570B3","#E7298A","#666666","#c241f8" ,"mediumaquamarine","#882E72",'#1965B0',"#bf812d")
A1<- c('#b2182b','#d6614d','#f4a582')
A2<- c('#8c510a','#bf812d','#F6C141')
A3<- c("#882E72", "#AE76A3","#D1BBD7")
A4<-  c('#01665e','#90C987','#CAE0AB')
A5<- c ('#1965B0','#4393c3',"#92c5de")
CO_ALU2=c("#d7191c","#d8b365","#542788","#abd9e9", "#2c7bb6", "#4d4d4d","grey")
CO_ALU=c("#d7191c","#542788","#d8b365","#abd9e9", "#2c7bb6", "#4d4d4d","grey")
CO_ALU3=c("#d7191c","#d8b365","#542788","grey", "#91bfdb", "grey","grey")
COe<- c(A1,A2,A3,A4,A5)
smartseq2 <-  RunUMAP(smartseq2, dims = 1:30, n.neighbors = 30)
smartseq2 <-  RunTSNE(smartseq2, dims = 1:20, check_duplicates = FALSE)
##### These PLots are printed later in the Script. here are to check #####
#DimPlot(smartseq2, reduction = "umap", label = T,cols = COe2,pt.size=0.7)
#DimPlot(smartseq2, reduction = "tsne", label = T,cols = COe2,pt.size=0.7)

#DimPlot(smartseq2, reduction = "umap",group.by ='GROUP',cols = CO_ALU,pt.size=0.7)
#DimPlot(smartseq2, reduction = "tsne",group.by ='GROUP',cols = COe,pt.size=0.7)

#DimPlot(smartseq2, reduction = "tsne", label = T)
#DimPlot(smartseq2, reduction = "tsne",group.by ='seurat_clusters', split.by = 'GROUP',cols = COe2,pt.size=0.7)
#DimPlot(smartseq2, reduction = "tsne",group.by ='GROUP', split.by = 'seurat_clusters',cols = CO_ALU3,pt.size=0.7)

#DimPlot(smartseq2, reduction = "umap",group.by ='GROUP',cols = CO_ALU3,pt.size=0.7)
#DimPlot(smartseq2, reduction = "tsne",group.by ='GROUP',cols = CO_ALU3,pt.size=0.7)

{pdf(file=paste(PTHA1,"01Umap_tsne_",PROJECT,"CLU_PC10_Dim30",".pdf",sep=""), width=8, height=3)  
P00a<- DimPlot(smartseq2, reduction = "umap", label = T,cols = COe2,pt.size=0.7)
P01a<- DimPlot(smartseq2, reduction = "umap", label = F,cols = COe2,pt.size=0.7)

P00at<- DimPlot(smartseq2, reduction = "tsne", label = T,cols = COe2,pt.size=0.7)
P01at<- DimPlot(smartseq2, reduction = "tsne", label = F,cols = COe2,pt.size=0.7)

P03<-DimPlot(smartseq2, reduction = "umap",group.by ='seurat_clusters', split.by = 'GROUP',cols = COe2,pt.size=0.7)
P03a<-  DimPlot(smartseq2, reduction = "umap",group.by ='GROUP', split.by = 'seurat_clusters',cols = CO_ALU3,pt.size=0.7)

P03t<-DimPlot(smartseq2, reduction = "tsne",group.by ='seurat_clusters', split.by = 'GROUP',cols = COe2,pt.size=0.7)
P03at<-  DimPlot(smartseq2, reduction = "tsne",group.by ='GROUP', split.by = 'seurat_clusters',cols = CO_ALU3,pt.size=0.7)
    
arrange1 <- ggarrange(P00a,P01a, ncol = 2,nrow =1,common.legend = T, align = c("hv"),legend="right")
arrange1t <- ggarrange(P00at,P01at, ncol = 2,nrow =1,common.legend = T, align = c("hv"),legend="right")
arrange1c <- ggarrange(P03, ncol = 1,nrow =1,common.legend = T, align = c("hv"),legend="right")
print(arrange1t)
print(arrange1)
}
dev.off()

## png 
##   2

{pdf(file=paste(PTHA1,"01Umap_tsne_",PROJECT,"CLU_PC10_Dim30_by_STAGE",".pdf",sep=""), width=12, height=3)  
arrange1bt <- ggarrange(P03t, ncol = 1,nrow =1,common.legend = T, align = c("hv"),legend="right")
arrange1ca <- ggarrange(P03a, ncol = 1,nrow =1,common.legend = T, align = c("hv"),legend="right")
arrange1cat <- ggarrange(P03at, ncol = 1,nrow =1,common.legend = T, align = c("hv"),legend="right")

print(arrange1bt)
print(arrange1c)
print(arrange1cat)
}
dev.off()

## png 
##   2

8.1 Plot UMAP and TSNE by Clusters

print(arrange1t)

print(arrange1)

print(arrange1bt)

print(arrange1c)

print(arrange1cat)

## png 
##   2

8.2 Plot UMAP and TSNE by GROUP

print(arrange3)

print(arrange3t)

print(arrange2)

9 Find top TFs for every Neighborhood ZONE

Settings could be changed, here we used the TF present in at least 10% of peaks and enriched in a log2FC>0.09. After the Likelihood-ratio test.

9.1 Barplot across the Neighborhood by GROUP

grid.arrange(  P20,P201, ncol = 1,nrow = 2)

9.2 Heatmap of the top 10 markers per neighborhood after the Likelihood-ratio test

grid.arrange(  P05ah_bino1, ncol = 1,nrow = 1)

grid.arrange(  P05ah_bino, ncol = 1,nrow = 1)

10 Handeling of the Seurat Object after Analysis

##Save Seurat Object - Important for further modification of the Plots. You do not need to start from the start of the script.

10.1 Extract analized matrix from Seurat objects

DATA_SC<- data.frame(smartseq2@assays[["RNA"]]@counts@i)
DATA_SC<- data.frame(smartseq2@assays[["RNA"]]@counts@i)
DATA_SC<- GetAssay(smartseq2,assay = "RNA")
DATA_SC1<- data.frame(DATA_SC@scale.data)
DATA_SC2<- data.frame(DATA_SC@scale.data)
#dim(DATA_SC1)
META_SEU<- data.frame(smartseq2@meta.data)
colnames(DATA_SC1)<- META_SEU$PositionID
colnames(META_SEU)[4]<- "SYMBOL_TF"

10.2 Selection of candidate genes from 04dpci marker list. This is performed after checking the output excel file

GENES001<- c("Twist2","Smad4", "Nanog","Smad2","Fos","Sox9","Sox10","JunB")
GENES002 <- c("Pitx1","Isl1",  "Mef2b","FOXA1","TEAD3","GATA3", "Gata6", "Mef2b")
GENES003 <- c("Eomes","Oct4","Foxh1","CUX1","Bapx1", "Elk4","Foxo1","Fli1")
GENES004<- c("Rbp7","Rgcc", "Fabp4","Egfl7","Flt1", "Cd36","Nrp1","Tpm1", "Rbp1")

11 Code to perform Violin plots of selected TFs

LE=10
SI=10
YL3="Norm binding of TFs"
XL3="clusters"
P1 <- VlnPlot(smartseq2, GENES001, stack = TRUE, sort = F, flip = T,pt.size=0.02)+geom_point(size = .4,colour = "darkgrey") +scale_fill_manual(values = COe2)+
  theme(legend.position = "none",plot.title= element_text(color=BASIC_COL, size=LE, face="bold"), axis.text.y= element_text(size=LE,face="bold"),axis.text.x = element_text(size=SI, face="bold"), axis.title.x =element_text(color=BASIC_COL, size=LE, face="bold"), axis.title.y = element_text(color=BASIC_COL, size=LE, face="bold")) +labs(title=paste(PROJECT, "SEVERAL_TFs",sep="" ) , x=XL3, y = YL3)


P1CM <- VlnPlot(smartseq2, GENES002, stack = TRUE, sort = F, flip = T,pt.size=0.02)+geom_point(size = .4,colour = "darkgrey") +scale_fill_manual(values = COe2)+
  theme(legend.position = "none",plot.title= element_text(color=BASIC_COL, size=LE, face="bold"), axis.text.y= element_text(size=LE,face="bold"),axis.text.x = element_text(size=SI, face="bold"), axis.title.x =element_text(color=BASIC_COL, size=LE, face="bold"), axis.title.y = element_text(color=BASIC_COL, size=LE, face="bold")) +labs(title=paste(PROJECT, "SEVERAL_TFs",sep="" ) , x=XL3, y = YL3)

P1CM3 <- VlnPlot(smartseq2, GENES003, stack = TRUE, sort = F, flip = T,pt.size=0.02)+geom_point(size = .4,colour = "darkgrey") +scale_fill_manual(values = COe2)+
  theme(legend.position = "none",plot.title= element_text(color=BASIC_COL, size=LE, face="bold"), axis.text.y= element_text(size=LE,face="bold"),axis.text.x = element_text(size=SI, face="bold"), axis.title.x =element_text(color=BASIC_COL, size=LE, face="bold"), axis.title.y = element_text(color=BASIC_COL, size=LE, face="bold")) +labs(title=paste(PROJECT, "SEVERAL_TFs",sep="" ) , x=XL3, y = YL3)
#P1CM3
#print(P1CM)

pdf(file=paste(PTHA1,"09_", PROJECT,"_CLU_vs_Ex_VIol_groups_STACKED",".pdf",sep=""), width=10, height=15)
 print(P1)
arrange1CM <- ggarrange(P1CM, ncol = 1,nrow =1,common.legend = T, align = c("hv"),legend="top")
arrange1CM3 <- ggarrange(P1CM3, ncol = 1,nrow =1,common.legend = T, align = c("hv"),legend="top")
dev.off()

## png 
##   2

11.1 Print Violin of Selected TFs by Neighborhood

print(arrange1CM)

print(arrange1CM3)

## png 
##   2

11.2 Print Violin of Selected TFs by Neighborhood splited by GROUPs

print(arrange1CM41)

print(arrange1CM42)

print(arrange1CM43)

## png 
##   2

11.3 VIolin plot splited by seurat Neighborhood Cluster

Plot in group to give an Overview of the TF across the Clusters
Alternative Violin plot of each TF in a plot (Optional to perform)

print(arrange12)

print(P04b12)

print(P04c12)

# Code for UMAP and TSNE plots

#FeaturePlot(smartseq2, features =GENES001,min.cutoff=0,max.cutoff=1,cols = CO3)
P05a<-FeaturePlot(smartseq2, features =GENES001,min.cutoff=0,max.cutoff=1,cols = CO3)
P05b<-FeaturePlot(smartseq2, features =GENES002,min.cutoff=0,max.cutoff=1,cols = CO3)
P05c<-FeaturePlot(smartseq2, features =GENES003,min.cutoff=0,max.cutoff=1,cols = CO3)

{pdf(file=paste(PTHA1,"09_",PROJECT,"_CLU_vs_Exp_UMMAP_groups",".pdf",sep=""),width=8, height=6)  
print(P05a)
print(P05b)
print(P05c)
}
dev.off()

## png 
##   2

P05a1<-FeaturePlot(smartseq2, features =GENES001,min.cutoff=0,max.cutoff=1,cols = CO3,reduction="tsne")
P05b1<-FeaturePlot(smartseq2, features =GENES002,min.cutoff=0,max.cutoff=1,cols = CO3,reduction="tsne")
P05c1<-FeaturePlot(smartseq2, features =GENES003,min.cutoff=0,max.cutoff=1,cols = CO3,reduction="tsne")

{pdf(file=paste(PTHA1,"09_",PROJECT,"_CLU_vs_Ex_TSNE_groups",".pdf",sep=""),width=8, height=6)  
print(P05a1)
print(P05b1)
print(P05c1)
}
dev.off()

## png 
##   2

12 Dimentional reduction plot for each selected TFs

12.1 UMAP plot

print(P05a)

print(P05b)

print(P05c)

12.2 TSNE plot

print(P05a1)

print(P05b1)

print(P05c1)

13 SessionInfo

sessionInfo()

## R version 4.3.0 (2023-04-21 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8   
## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C                   
## [5] LC_TIME=German_Germany.utf8    
## 
## time zone: Europe/Berlin
## tzcode source: internal
## 
## attached base packages:
##  [1] stats4    splines   grid      parallel  stats     graphics  grDevices
##  [8] utils     datasets  methods   base     
## 
## other attached packages:
##  [1] future_1.33.0               readxl_1.4.2               
##  [3] dplyr_1.1.2                 stringr_1.5.0              
##  [5] khroma_1.10.0               tidyr_1.3.0                
##  [7] EnrichedHeatmap_1.30.0      gplots_3.1.3               
##  [9] plyr_1.8.8                  org.Mm.eg.db_3.17.0        
## [11] AnnotationDbi_1.62.2        SeuratObject_4.1.3         
## [13] Seurat_4.3.0.1              clusterProfiler_4.8.1      
## [15] Rcpp_1.0.10                 RColorBrewer_1.1-3         
## [17] DESeq2_1.40.2               SummarizedExperiment_1.30.2
## [19] Biobase_2.60.0              MatrixGenerics_1.12.2      
## [21] matrixStats_1.0.0           GenomicRanges_1.52.0       
## [23] GenomeInfoDb_1.36.1         IRanges_2.34.1             
## [25] S4Vectors_0.38.1            BiocGenerics_0.46.0        
## [27] RcppArmadillo_0.12.4.1.0    gam_1.22-2                 
## [29] BiocParallel_1.34.2         circlize_0.4.15            
## [31] ComplexHeatmap_2.16.0       doParallel_1.0.17          
## [33] iterators_1.0.14            foreach_1.5.2              
## [35] ggpubr_0.6.0                ggplot2_3.4.2              
## [37] edgeR_3.42.4                limma_3.56.2               
## [39] gridExtra_2.3               data.table_1.14.8          
## [41] openxlsx_4.2.5.2            knitr_1.43                 
## [43] BiocStyle_2.28.0           
## 
## loaded via a namespace (and not attached):
##   [1] spatstat.sparse_3.0-2   bitops_1.0-7            enrichplot_1.20.0      
##   [4] HDO.db_0.99.1           httr_1.4.6              tools_4.3.0            
##   [7] sctransform_0.3.5       backports_1.4.1         utf8_1.2.3             
##  [10] R6_2.5.1                uwot_0.1.15             lazyeval_0.2.2         
##  [13] GetoptLong_1.0.5        withr_2.5.0             sp_2.0-0               
##  [16] progressr_0.13.0        cli_3.6.1               spatstat.explore_3.2-1 
##  [19] scatterpie_0.2.1        labeling_0.4.2          sass_0.4.6             
##  [22] spatstat.data_3.0-1     ggridges_0.5.4          pbapply_1.7-2          
##  [25] yulab.utils_0.0.6       gson_0.1.0              DOSE_3.26.1            
##  [28] parallelly_1.36.0       rstudioapi_0.14         RSQLite_2.3.1          
##  [31] generics_0.1.3          gridGraphics_0.5-1      shape_1.4.6            
##  [34] gtools_3.9.4            spatstat.random_3.1-5   ica_1.0-3              
##  [37] car_3.1-2               zip_2.3.0               GO.db_3.17.0           
##  [40] Matrix_1.5-4.1          ggbeeswarm_0.7.2        fansi_1.0.4            
##  [43] abind_1.4-5             lifecycle_1.0.3         yaml_2.3.7             
##  [46] carData_3.0-5           qvalue_2.32.0           Rtsne_0.16             
##  [49] blob_1.2.4              promises_1.2.0.1        crayon_1.5.2           
##  [52] miniUI_0.1.1.1          lattice_0.21-8          cowplot_1.1.1          
##  [55] KEGGREST_1.40.0         magick_2.7.4            pillar_1.9.0           
##  [58] fgsea_1.26.0            rjson_0.2.21            future.apply_1.11.0    
##  [61] codetools_0.2-19        fastmatch_1.1-3         leiden_0.4.3           
##  [64] glue_1.6.2              downloader_0.4          ggfun_0.1.1            
##  [67] vctrs_0.6.3             png_0.1-8               treeio_1.24.1          
##  [70] cellranger_1.1.0        gtable_0.3.3            cachem_1.0.8           
##  [73] xfun_0.39               S4Arrays_1.0.4          mime_0.12              
##  [76] tidygraph_1.2.3         survival_3.5-5          ellipsis_0.3.2         
##  [79] fitdistrplus_1.1-11     ROCR_1.0-11             nlme_3.1-162           
##  [82] ggtree_3.8.0            bit64_4.0.5             RcppAnnoy_0.0.20       
##  [85] bslib_0.5.0             irlba_2.3.5.1           vipor_0.4.5            
##  [88] KernSmooth_2.23-21      colorspace_2.1-0        DBI_1.1.3              
##  [91] ggrastr_1.0.2           tidyselect_1.2.0        bit_4.0.5              
##  [94] compiler_4.3.0          DelayedArray_0.26.6     plotly_4.10.2          
##  [97] bookdown_0.34           shadowtext_0.1.2        caTools_1.18.2         
## [100] scales_1.2.1            lmtest_0.9-40           goftest_1.2-3          
## [103] digest_0.6.31           spatstat.utils_3.0-3    rmarkdown_2.23         
## [106] XVector_0.40.0          htmltools_0.5.5         pkgconfig_2.0.3        
## [109] highr_0.10              fastmap_1.1.1           rlang_1.1.1            
## [112] GlobalOptions_0.1.2     htmlwidgets_1.6.2       shiny_1.7.4            
## [115] farver_2.1.1            jquerylib_0.1.4         zoo_1.8-12             
## [118] jsonlite_1.8.5          GOSemSim_2.26.0         RCurl_1.98-1.12        
## [121] magrittr_2.0.3          GenomeInfoDbData_1.2.10 ggplotify_0.1.1        
## [124] patchwork_1.1.2         munsell_0.5.0           ape_5.7-1              
## [127] viridis_0.6.3           reticulate_1.30         stringi_1.7.12         
## [130] ggraph_2.1.0            zlibbioc_1.46.0         MASS_7.3-60            
## [133] listenv_0.9.0           ggrepel_0.9.3           deldir_1.0-9           
## [136] Biostrings_2.68.1       graphlayouts_1.0.0      tensor_1.5             
## [139] locfit_1.5-9.8          igraph_1.5.0            spatstat.geom_3.2-1    
## [142] ggsignif_0.6.4          reshape2_1.4.4          evaluate_0.21          
## [145] BiocManager_1.30.21     tweenr_2.0.2            httpuv_1.6.11          
## [148] RANN_2.6.1              purrr_1.0.1             polyclip_1.10-4        
## [151] clue_0.3-64             scattermore_1.2         ggforce_0.4.1          
## [154] broom_1.0.5             xtable_1.8-4            tidytree_0.4.2         
## [157] rstatix_0.7.2           later_1.3.1             viridisLite_0.4.2      
## [160] tibble_3.2.1            aplot_0.1.10            beeswarm_0.4.0         
## [163] memoise_2.0.1           cluster_2.1.4           globals_0.16.2

03b_TFinZONE_at_14dpci

18 Januar 2024

Contents